Lecture 14: Multivariate Distributions Probability Theory and Applications Fall 2005 October 25.
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Transcript of Lecture 14: Multivariate Distributions Probability Theory and Applications Fall 2005 October 25.
![Page 1: Lecture 14: Multivariate Distributions Probability Theory and Applications Fall 2005 October 25.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e725503460f94b7217d/html5/thumbnails/1.jpg)
Lecture 14: Multivariate Distributions
Probability Theory and Applications
Fall 2005
October 25
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Multivariate Distributions
Distributions may have more than one R.V.
Example: S=size of house - real RV P=price of house - real RV A=Age of house - real RV C= condition of house Excellent, Very Good, Good, Poor
- discrete RV
Since variables are not-independent need a multivariate distribution to describe them: f(S,P,A,C)
![Page 3: Lecture 14: Multivariate Distributions Probability Theory and Applications Fall 2005 October 25.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e725503460f94b7217d/html5/thumbnails/3.jpg)
Bivariate Random Variables
Given R.V. X and YCases1. X,Y both discrete number of blue and red jelly beans picked from jar2. X,Y both continuous height and weight3. X discrete and Y continuous date and stock price
![Page 4: Lecture 14: Multivariate Distributions Probability Theory and Applications Fall 2005 October 25.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e725503460f94b7217d/html5/thumbnails/4.jpg)
Both Discrete
The joint distribution of (X,Y) is specified by
• The value set of (X,Y)
• The joint probability function
f(x,y)=P(X=x,Y=y)
Note:
• f(x,y)≥0 for any (x,y)
( , ) 1x y
f x y
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Discrete Example
Box contains jewels H=high quality
M=medium quality D=defective
You pick two jewels w/o replacementX=# of HY =#of M
3 H
2 M
2 D
2
2 1( 0, 0)
7 21
2
P X Y
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Joint Probability Function
X\Y 0 1 2
0 1/21 4/21 1/21 6/21
1 6/21 6/21 0/21 12/21
2 3/21 0/21 0/21 3/21
10/21 10/21 1/21
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Joint Probability Function
X\Y 0 1 2
0 1/21 4/21 1/21
1 6/21 6/21 0/21
2 3/21 0/21 0/21
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Marginal Probability Functions
X\Y 0 1 2
0 1/21 4/21 1/21 6/21
1 6/21 6/21 0/21 12/21
2 3/21 0/21 0/21 3/21
10/21 10/21 1/21 1( )Yf y
( )Xf x
![Page 9: Lecture 14: Multivariate Distributions Probability Theory and Applications Fall 2005 October 25.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e725503460f94b7217d/html5/thumbnails/9.jpg)
Definitions
The marginal distribution of X is
Note this is exactly the same as pdf of X
The joint cumulative density function of X,Y is
( ) ( , )Xy valueset
f x f x y
( , ) ( , )F x y P X x Y y
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Questions
P(You get one high quality and one medium jewel)?
P(You pick at least one high quality jewel)?
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Conditional Distributions
The conditional distribution of Y given X is
In our example:
( , )( | ) ( | )
( )
P Y y X xf y x P Y y X x
P X x
(1)
( 1, ) (1, )( | 1)
( 1) x
P x y f yf y x
P X f
![Page 12: Lecture 14: Multivariate Distributions Probability Theory and Applications Fall 2005 October 25.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e725503460f94b7217d/html5/thumbnails/12.jpg)
Conditional Probability Functions
X\Y 0 1 2
0 1/21 4/21 1/21 6/21
1 6/21 6/21 0/21 12/21
2 3/21 0/21 0/21 3/21
10/21 10/21 1/21 1( )Yf y
( )Xf x
Y 0 1 2
f(y|X=1)1
6 / 21
2 / 21 1
0 / 21
2 / 211
6 / 21
2 / 21
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Conditional Probability Functions
X\Y 0 1 2
0 1/21 4/21 1/21 6/21
1 6/21 6/21 0/21 12/21
2 3/21 0/21 0/21 3/21
10/21 10/21 1/21 1( )Yf y
( )Xf x
X 0 1 2
f(x|Y=1) 4/10 6/10 0
Find distribution ofX given Y=1
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Question
Given that exactly one jewel picked is medium quality, what is the probability that the other is high quality?
6/10
Given that at least one jewel picked is medium quality, what is the probability that the other is high quality?
6/11
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X,Y both Continuous
The joint pdf, f(x,y) defined over R2 has properties:
• f(x,y)≥0
To calculate probabilities, integrate joint pdf over X,Y over the area
Or more generally if we want P((x,y)A)
( , ) 1f x y dxdy
( , ) ( , )b d
a c
P a X b c Y d f x y dydx
( , ) ) ( , )A
P X Y A f x y dA
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X,Y both Continuous
More generally if we want P((x,y)A)
The c.d.f.
( , ) ) ( , )A
P X Y A f x y dA
( , ) ( , )
( , )u v
F x y P X x Y y
f u v dvdu
2 ( , )( , ) . .
F x yf x y c d f
x y
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Marginals and Conditionals
The marginal pdf of X
The marginal pdf of Y
The conditional pdf of X given Y=y
( ) ( , )xf x f x y dy
( ) ( , )yf y f x y dx
( , )( | )
( )y
f x yf x y
f y
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Examples
The joint pdf of (x,y) is
Find c
( , ) (1 ) 0 1, 0 2f x y cx y for X y
1 2 1 200 0 0
1
0
1 (1 ) (1 ) |
4 2
1/ 2
c x y dydx c x y dx
c xdx c
c
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continued
Find pdf of X
Find pdf of Y
2 2
0 0
1( ) ( , ) (1 ) 2
2Xf x f x y dy x y dy x
1 1
0 0
1( ) ( , ) (1 ) 1/ 4(1 )
2Yf y f x y dx x y dx y
2 0 1( )
0 . .X
x xf x
o w
1/ 4(1 ) 0 2( )
0 . .Y
y yf y
o w
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continued
Find marginal of X given Y=1
Note this is the same as marginal of X!
X and Y are independent!
( , 1) 1/ 2 (1 1)( | 1) 2
(1) 1/ 4(1 1)Y
f x y xf x y x
f
|
2 0 1( | 1)
0 . .X Y
x xf x y
o w
( , ) 1/ 2 (1 )( | ) 2 0 1
( ) 1/ 4(1 )Y
f x y x yf x y x x
f y y
![Page 21: Lecture 14: Multivariate Distributions Probability Theory and Applications Fall 2005 October 25.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e725503460f94b7217d/html5/thumbnails/21.jpg)
continued
Find P(X>Y)
1
0 0
21
00
13 3 4
1 2
00
( ) (1 )2
1
2 2
1 1 1(1/ 3 1/ 8) 11/ 48
2 2 2 3 8 2
x
x
xP X Y y dydx
yx y dx
x x xx dx
? ?
? ?( ) (1 )
2
xP X Y y dydx 0 X 1
2
Y
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Mixed Continuous and Discrete
Let L a be R.V. that is 1 if candy corn manufactured from Line 1 and 0 if line 0
Let X=weight of candy corn
The joint pdf is
What is the marginal distribution of X – the weight of the candy corn?
2
2
( 7.05)
2
( 10.1)
2(1.44),
10.25
12
1( , ) 0.75
02 1.2
0 . .
x
x
X L
xe
L
xf x l e
L
o w
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Mixed Continuous and Discrete
The joint pdf is
Sum over L to find the marginal of X
22 ( 10.1)( 7.05)2(1.44)2
( ) ( ,1) ( ,0)
1 10.25 0.75
2 2 1.2
x
xx
f x f x f x
e e x
2
2
( 7.05)
2
( 10.1)
2(1.44),
10.25
12
1( , ) 0.75
02 1.2
0 . .
x
x
X L
xe
L
xf x l e
L
o w
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Conditional Distribution
What is the marginal of L?
What is the conditional X given L?
2
2
( 7.05)
2
( 10.1)
2(1.44)
10.25 0.25 1
2
1( , ) 0.75 0.75 0
2 1.2
.
x
x
L
e dx L
f x l e dx L
2
2
( 7.05)
2
( 10.1)
2(1.44)
11
2
01( | )
2 1.2
.
x
x
Le
x
Lf x l e
x
L is Bernoulli R.V. p=0.25
If candy corn is from Line 1,weight is normal with mean 7.05 and s.d. = 1.If candy corn is from Line 0,weight is normal withmean 10.1 and s.d. = 1.2.
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Mixture Model
X is a mixture of two different normals
0 5 10 15
0
0.05
0.1
0.15
0.2
0.25
x
0.25 1/(sqrt(2 )) exp(-(x-7.05)2/2)+0.75 1/(sqrt(2 1.44)) exp(-(x-10.1)2/(2 1.44))
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Example 5
Harry Potter plays flips a magical coin 10 times and records the number of heads.
The coin is magical because each day the probability of getting heads changes.
Let Y, the probability of getting heads on a given day, be uniform [0,1]
Let X be the number of heads of 10 gotten on a given day with the magic coin.
What is the pdf of X?
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Example 5 continued
Y is uniform [0,1] so
X|Y is binomial n=10 p=Y
So f(X,Y)
( ) 1 0 1Yf y y
1010( , ) (1 ) 0,1, ,10, 0 1x xf X Y y y x y
x
110
0
10( ) (1 ) Note this is a Beta Integral!
10 ( 1) (10 1) 10! !(10 )!_
( 1 10 1) (10 )! ! 11!
10,1, ,10
11
x xXf X y y dy
x
x x x x
x x x x x
x
X is discrete uniformAll values equally likely
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Fact
You can compute the joint from a marginal and a conditional.
Be careful how you compute the value sets!
( , ) ( | ) ( )xf x y f y x f x
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Example 2 – Two Continuous
The joint pdf of X and Y is
Find marginal of X
( , ) (1 ) 0 1f x y cx y x y
O X 1
Y
1
11 2
2
( ) (1 ) / 2
1 1/ 2 / 2
X x xf x cx y dy cx y y
cx x x
2 3/ 2 / 2 0 1( )
0 . .X
c x x x xf x
o w
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Example 2
Still need c
You check:
12 3
0
1 / 2 / 2 / 24 1 24c x x x dx c c
212 (1 ) 0 1( )
0 . .X
x x xf x
o w
212 (1 ) 0 1( )
0 . .Y
y y yf y
o w
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continued
Find P(Y<2X)
1 / 2
0 0( , ) 1/ 4
yf x y dxdy 1
0 / 2( , ) 3 / 4
y
yf x y dxdy
OX 1
Y
1
OX 1
Y
1
P(Y≥2X)
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Conditional distribution
Find conditional pdf of Y and X=1/2
O X 1
Y
1
(1/ 2, )( | 1/ 2)
(1/ 2)x
f yf y x
f ? ?y 1/ 2 1y
2
24 /(1/ 2)(1 )( | 1/ 2)
12 / 2(1 1/ 2)
8(1 )
yf y x
y
\ 1/ 2
8(1 ) 1/ 2 1( | 1/ 2)
0 . .Y X
y yf y x
o w
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Conditional distribution
Find conditional pdf of Y and X=x 0<x<1
O X 1
Y
1 2\ 1/ 2
2(1 )1
( | ) 10 . .
Y X
yx y
f y x xo w
![Page 34: Lecture 14: Multivariate Distributions Probability Theory and Applications Fall 2005 October 25.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e725503460f94b7217d/html5/thumbnails/34.jpg)
Independence
R.V. X and Y are independent if and only any of the following hold
1. F(x,y)=FX(x)FY(y)
P(X≤x,Y≤y)= P(X≤x)P(Y≤y)
2. f(x,y)=fX(x)fY(y)
3. f(y|x)=fY(y)
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Example 3
Given the joint pdf of X,Y
Use the marginal of X and the conditional pdf of Y given X=x to determine if X and Y are independent?
( , ) 8 0 1f x y xy x y
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Answer
Find marginal of X
Find conditional of Y given X
1
2
( ) 8
4 (1 ) 0 1
x
x
f x xydy
x x x
O
Y
1
2 2
8 2( | ) 0 1
4 (1 ) 1
xy yf y x x y
x x x
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Answer continued
Are they independent?
No
2 3( ) ( ) 4 (1 )4 8 ( , )x yf x f y x x y xy f x y
3
0
( ) 8 4 0 1y
yf y xydx y y
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Note
P(Y≤3/4|x=1/2) and P(Y≤3/4|x ≤1/2) are very different things!
Let’s calculate each one
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P(Y≤3/4|X=1/2)
The pdf of Y given X=1/2 is
so3/ 4 3/ 4
1/ 2 1/ 2
2( 3/ 4 | 1/ 2) ( | 1/ 2) 5 /12
0.75
yP Y X f y x dy dy
2
21/ 2 1
1 (1/ 2)
yy
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P(Y≤3/4|X ≤ 1/2)
The probability Y given X ≤ 1/2 is
where1/ 2 1/ 2
2
0 0
( 1/ 2) ( ) 4 (1 ) 7 /16xP X f x dx x x dx
( 3 / 4, 1/ 2)( 3 / 4 | 1/ 2)
( 1/ 2)
P Y XP Y X
P X
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P(Y≤3/4|X ≤ 1/2)
The probability P(Y≤3/4,X ≤ 1/2)
The probability
1/ 2 3/ 4
0
1/ 23/ 42
0
1/ 22
0
( 3 / 4, 1/ 2) 8
4
4 (9 /16 ) 7 / 32
x
x
P Y X xydydx
x y dx
x x dx
O
1
7 / 32( 3/ 4 | 1/ 2) 1/ 2
7 /16P Y X
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Example 4
Suppose X has the Gamma distribution with parameters with K=2 and theta=1 and
the conditional distribution of Y given X.
(X>0) is
Find P( X<4| Y=2)
( | ) 1/ 0f y x x y x
( ) 0xxf x e x x
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Example 4
We know f(x,y)=f(x|y)fx(x) so the joint is
The marginal of Y is
Thus conditional of X given Y is
1( , ) 0xf x y e x y x
x
( ) 0x yy
y
f y e dx e y
( | ) 0x
y
ef x y y x for y
e
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Example 4 continued
So
Thus
Exercise try: P(X>4|Y>2)
1/ 2( | 1/ 2) 1/ 2
xef x y x
e
42
1/ 21/ 2
( 4 | 1/ 2)xe
P x y dx ee
![Page 45: Lecture 14: Multivariate Distributions Probability Theory and Applications Fall 2005 October 25.](https://reader030.fdocuments.in/reader030/viewer/2022032708/56649e725503460f94b7217d/html5/thumbnails/45.jpg)
Example 5 – Two Discretes
You write a paper with an average rate of 10 errors per paper. Assume the number of errors per papers follows a Poisson distribution.
You roommate proofreads it for you, and he/she has .8 percent of correcting each error.
What is the joint distributions of the number of errors and the number of corrections?
What is the distribution of the number of errors after you roommate reads the paper?
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answer
Let X be the number of errors
Y be the number of errors after correction
Clearly Y depends on X.
Given
What is pdf of Y|X?
binomial(n=X,p=.2)
( ) ~ ( 10)Xf x Poisson
( | ) .2 .8 0, ,y x yxf y x y x
y
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answer
Let X be the number of errors
Y be the number of errors after correction
,
x 10
( , ) ( | ) ( )
10.2 .8 0, 0, ,
x!
X Y x
y x y
f x y f y x f x
x ex y x
y
Extra Credit: if you can figure out marginal of Y.